Search results

This study explores the direct relationship between social media user-generated content (UGC), online search traffic and offline light vehicle sales of different models.

The long-run equilibrium relationship and short-run dynamic effects between the valence and volume of UGC, online search traffic and offline car sales are analyzed by applying the autoregressive distribution lag (ARDL) model.

The study found the following. (1) In the long-run relationship, the valence of online reviews on social media platforms is significantly negatively correlated with the sales of all models. However, in the short-run, the valence of online reviews has a significant positive correlation with all models in different lag periods. (2) The volume of online reviews is significantly positively correlated with the sales of all models in the long run. However, in the short run, the relationship between the volume of online reviews and the sales of lower-sales-volume cars is uncertain. There is a significant positive correlation between the volume of reviews and the sales of higher-sales-volume cars. (3) Online search traffic has a significantly negative correlation with the sales of all models in the long run. However, in the short run, there is no consistent conclusion on the relationship between online search traffic and car sales.

This study provides a reference for managers to use in their efforts to improve offline high-involvement product sales using online information.

Online web searches have played crucial roles in influencing consumers’ purchasing decisions. Web search traffic information enables researchers and practitioners to better understand consumers in terms of their preferences and interests, among other things. The purpose of this paper is to use web search traffic information provided by Google Trends to derive relationships among product brands as well as those between product brands and product attributes to propose a method to enhance the visibility of consumer brand positioning.

This study builds upon the interesting observation that consumers’ behavior in performing simultaneous searches, or searches including two or more keywords, can be converted into data indicating relationships among brands as well as those between brands and their attributes. The study focuses on the cases of hybrid cars and tablet PCs, and applies a social network analysis method to identify these relationships. Time series information on web search traffic is used because it can track these two product groups from the early stages to the present. This step is completed to verify the changes in the status of each brand and in their relationships that occurred in consumers’ minds over time.

Results show that consumers’ web search behaviors reveal the brand positioning and brand-attribute associations in their minds. Specifically, using consumers’ simultaneous search data, the authors derived relationships among brands (brand-brand network) from consumers’ behaviors of searching simultaneously for two brands and the relationships between brands and attributes (brand-product attributes network) from consumers’ behavior of searching simultaneously for a specific brand and certain product attributes.

Theoretically, this study verifies that consumers’ web search traffic information can be used to microscopically identify the positions of individual brands and their relationships in the minds of consumers. Regarding practical applications, this study proposes a method that can be used by companies to track how consumers perceive their brands by performing a simple and cost-effective analysis using the free search traffic information provided by Google.

The application of “Google” econometrics (Geco) has evolved rapidly in recent years and can be applied in various fields of research. Based on accepted theories in existing economic literature, this paper seeks to contribute to the innovative use of research on Google search query data to provide a new innovative to property research.

In this study, existing data from Google Insights for Search (GI4S) is extended into a new potential source of consumer sentiment data based on visits to a commonly‐used UK online real‐estate agent platform (Rightmove.co.uk). In order to contribute to knowledge about the use of Geco's black box, namely the unknown sampling population and the specific search queries influencing the variables, the GI4S series are compared to direct web navigation.

The main finding from this study is that GI4S data produce immediate real‐time results with a high level of reliability in explaining the future volume of transactions and house prices in comparison to the direct website data. Furthermore, the results reveal that the number of visits to Rightmove.co.uk is driven by GI4S data and vice versa, and indeed without a contemporaneous relationship.

This study contributes to the new emerging and innovative field of research involving search engine data. It also contributes to the knowledge base about the increasing use of online consumer data in economic research in property markets.

Webrooming (i.e. searching information online and making the final purchase in a physical store) has become a popular shopping practice, but remains insufficiently studied. To address this, a research framework encompassing online and offline channel attributes (i.e. online review diagnosticity, online search convenience, expected price loss, offline purchase effort and offline after-sales service convenience), consumer traits (i.e. anticipated regret) and shopping experience (i.e. smart-shopping perception) as determinants of webrooming continuance intention is proposed.

The proposed model was validated by conducting a questionnaire-based survey that yielded 354 useable responses. The data was subjected to partial least squares structural equation modelling and importance-performance map analysis.

According to the obtained results, online review diagnosticity, offline after-sales service convenience and anticipated regret are the vital antecedents of webrooming continuance intention, while smart-shopping perception acts as the mediator.

The current study adds significantly to the body of knowledge about webrooming by validating the inter-relationships between online review diagnosticity, after-sales service convenience, anticipated regret, smart-shopping perception and webrooming continuance intention.

The use of commercial, online search services has increased dramatically in recent years. This reflects both an increasing awareness of their potential value on the part of end‐users and the technological advances which have improved access and availability. This paper examines the driving forces for improving the manipulation, presentation and management of downloaded bibliographic data to enhance its information content and value to the user. A number of opportunities for data post‐processing are highlighted and discussed.

The authors hope to filter valuable information from online reviews, obtain objective and accurate information about the demands of auto consumers and help auto companies develop more reasonable production and marketing strategies for healthy and sustainable development. This paper aims to discuss the aforementioned objectives.

The authors collected review data from online automotive forums and generated a corpus after pre-processing. Then, the authors extracted consumer demands and topics using the LDA model. Finally, the authors used a trained Word2vec tool to extend the consumer demand topics.

Different types of vehicle consumers have the same demands, such as “Space,” “Power Performance,” and “Brand Comparison,” and distinct demands, such as “Appearance,” “Safety,” “Service,” and “New Energy Features”; consumers who buy new energy vehicles are still accustomed to comparing with the brands or models of fuel vehicles; new energy vehicles consumers pay more attention to services and service quality during the purchasing and using process.

The development time of new energy vehicles is relatively short, with some models being available for only one year or even six months. The smaller amount of available data may impact the applicability of topic models. The sample size, especially for new energy vehicles, needs to be increased to improve the general applicability of topic models further.

First, this measure helps online review websites improve their existing review publication mechanisms, enhance the overall quality of online review content, increase user traffic and promote the healthy development of online review websites. Second, this allows for timely adjustments in future product production and sales plans and further enhances automotive companies' ability to leverage online reviews for Internet marketing.

The authors have improved the accuracy and stability of the fused topic model, providing a scientific and efficient research tool for multi-dimensional topic mining of online reviews. With the help of research results, consumers can more easily understand the discussion topics and thus filter out valuable reference information. As a result, automotive companies may gain information about consumer demands and product quality feedback and thus quickly adjust production and marketing strategies to increase sales and market share.

The study aims to help epidemiologists identify new patterns and trends in spreading infections on the example of the current coronavirus disease 2019 (COVID-19) pandemic using data from search engines. The study identified the types of thematic search of Russian Internet users and queries that have a mathematically confirmed correlation with public health indicators: mortality and morbidity from COVID-19. The study aims to determine digital epidemiology search trends to the current COVID-19 pandemic. The study identified the types of thematic search of RuNet users and queries that have a mathematically confirmed correlation with public health indicators: mortality and morbidity from COVID-19.

The authors explored two types of data: (1) the monthly datasets of keywords relevant to COVID-19 extracted from the Yandex search engine and (2) officially published statistics data. Alongside, the authors searched for associations between all variables in this dataset. The Benjamin–Hochberg correction for multiple hypothesis testing was applied to the obtained results to improve the reliability of the results. The authors built a unique website with opportunities to update datasets and designed dashboards to visualize the research outcomes using PHP and Python.

The research results show the number of significant relationships that the authors interpreted in epidemiology as a new instrument in Public Health research. There are 132 data combinations with a correlation higher than 75%, making it possible to determine a mathematically reliable relationship between search statistics trends and mortality/morbidity indicators. The most statistically significant effects identified in bundles “query” – “query”, “query” – “morbidity”, “query” – “mortality”.

The authors developed a new approach in analyzing outbreaks of infections and their consequences based on a comprehensive analysis of epidemiological and infodemic data. The research results are relevant to public health as other decision-making and situational analysis tools for citizens and specialists who want to receive additional confirmation for the indicators of the official statistics of the headquarters for control and monitoring of the situation with coronavirus and others infections.

For the past two years or so, there's been a lot of discussion about compact discs and how they will affect libraries. Now that so many compact disc products are available, you may be considering which one(s) to purchase and wondering what such a system will mean for your library. The following guidelines may help you evaluate the various products and plan for their easy integration into your library.

This chapter conceptualises a link between Industrial Revolution 4.0 (IR 4.0), big data, data science and sustainable tourism.

The author adopts a grounded theory and conceptual approach to endeavour in this exploratory research.

The outcome shows a significant rise of big data in the tourism sector under three major dimensions, i.e. business, governance and research. And, some exemplary evidence of institutions promoting the use of big data and data science for sustainable tourism has been discussed.

The conceptualised interlinkage of concepts like IR 4.0, big data, data science and sustainable development provides a valuable knowledge resource to policy-makers, researchers, businesses and students.

The purpose of this paper is to utilize co-query volumes of brands as relatedness measurement to understand the market structure and demonstrate the usefulness of brand relatedness via a real-world case.

Using brand relatedness measurement obtained using data from Google Trends as data inputs into a multidimensional scaling method, the market structure of the automobile industry is presented to reveal its competitive landscape. The relatedness with brands involved in product-harm crisis is further incorporated in empirical models to estimate the influence of crisis on future sales performance of each brand. A representative incident of a product-harm crisis in the automobile industry, which is the 2009 Toyota recall, is investigated. A panel regression analysis is conducted using US and world sales data.

The use of co-query as brand relatedness measurement is validated. Results indicate that brand relatedness with a brand under crisis is positively associated with future sales for both US and global market. Potential presence of negative spillovers from an affected brand to innocent brands sharing common traits such as same country of origin is shown.

The brand relatedness measured from co-query volumes is considered as a broad concept, which encompasses all associative relationships between two brands perceived by the consumers. This study contributes to the literature by clarifying the concept of brand relatedness and proposing a measure with readily accessible data. Compared to previous studies relying on a vast amount of online data, the proposed measure is proven to be efficient and enhance predictions about the future performance of brands in a turbulent market.